1

National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention

Whole-genome sequencing for investigation of recent 

TB transmission in the United States: Current uses and 

future plans 

Sarah Talarico, PhD and Benjamin Silk, PhD 

Surveillance, Epidemiology, and Outbreak Investigations Branch

Tambi Shaw, MPH and Martin Cilnis, MPH, MS

TB Control Branch/California Department of Public Health

Division of Tuberculosis Elimination

Hello, I’m Sarah Talarico. I’m an epidemiologist with the Molecular Epidemiology Activity in the Division 

of Tuberculosis Elimination at CDC

I will be presenting the following set of training slides focused on the use of whole-genome sequencing 

for investigating recent TB transmission along with colleagues, Tambi Shaw and Martin Cilnis, from the 

TB Control Branch at the California Department of Public Health
2

Learning objectives

At the end of this presentation, participants will be able to 

describe

 Key differences between conventional genotyping and WGS

 What is being represented on a phylogenetic tree

 How WGS is used to assess whether patients are potentially 

linked by recent transmission

 Why WGS alone cannot be used to infer direction of 

transmission

 How TB control programs can use WGS analysis in an 

investigation

At the end of this presentation, participants will be able to describe:

Key differences between conventional genotyping and WGS

What is being represented on a phylogenetic tree

How WGS is used to assess whether patients are potentially linked by recent transmission

Why WGS alone cannot be used to infer direction of transmission

How TB control programs can use WGS analysis in an investigation
3

Outline

 Part 1: Introduction to using whole-genome sequencing (WGS) for 

detection and investigation of recent TB transmission

– Goals of TB molecular epidemiology

– Current genotyping methods

– Use of WGS for investigating TB transmission

– Guide for interpreting results of WGS analysis

 Part 2: Case studies using WGS to investigate TB cluster alerts in 

California

– 2 case studies with WGS and epidemiologic data

– One confirmed outbreak and one refuted outbreak

 Part 3: Plans for transition to universal prospective WGS

This presentation is divided into three parts

Part 1 is an introduction to using whole-genome sequencing (or WGS) for detection and investigation of 

recent TB transmission

I will cover the goals of TB molecular epidemiology, current genotyping methods, use of WGS for 

investigating TB transmission, and a guide for interpreting results of WGS analysis

Part 2 will be case studies using WGS to investigate TB cluster alerts in California

Martin and Tambi will present two case studies with WGS and epi data, one is a confirmed outbreak 

and one is a refuted outbreak

Then in Part 3, I will briefly describe the plans for transition to universal prospective WGS. A separate 

presentation covering the details of how universal prospective WGS will be implemented will be made 

available in the future
4

Part 1

Using WGS for detection and investigation of recent TB 

transmission 

First I will go over some background for how to use WGS for detection and investigation of recent TB 

transmission
5

TB Transmission and Course of Infection

Active Disease

(infectious)

Latent Infection

(not infectious)

~5% within 2 yrs.

(recent transmission)

~95%

~5% lifetime risk

(reactivation)

cdc.gov

TB is caused by Mycobacterium tuberculosis and is transmitted through the airborne route

5% of people who are infected develop active TB disease within 2 years. These people are infectious 

and can carry on the chain of transmission

The other 95% develop latent TB, which is not infectious

However, about 5% of people with latent TB will reactivate and develop active disease at some point in 

their life

So two main strategies for eliminating TB are detecting and treating latent TB infection and detecting 

and interrupting ongoing transmission, which is the focus for today’s presentation
6

TB Molecular Epidemiology: Targeting Recent 

Transmission

 Goal

– Reduce the burden of TB by identifying where transmission is currently 

occurring and interrupting it

 Challenge

– Distinguish recent transmission from cases infected long ago

 Approach

– Combine molecular, clinical, and epidemiologic data to detect, investigate, 

and monitor recent TB transmission

TB molecular epidemiology targets recent transmission with the goal of reducing the burden of TB by 

identifying where transmission is currently occurring and interrupting it

The challenge is we need to distinguish TB cases that are due to recent transmission from cases that 

were infected long ago and are just now developing active disease

We do this by combining molecular, clinical, and epidemiologic data to detect, investigate, and monitor 

recent transmission
7

Why combine molecular, clinical, and epidemiologic data 

to understand TB transmission?

 Challenges to relying exclusively on epidemiologic investigation

– Airborne transmission

– Exposure in congregate settings

– Long infectious periods

– Patient recall may be incomplete or unreliable

– Often in impoverished or marginalized communities

 Molecular genotyping data can provide additional, complementary 

information to aid detection and investigation of transmission

– Genotyping identifies cases with genetically similar M. tuberculosis isolates that 

are more likely to be linked by transmission

We combine molecular data with clinical and epi data because there are challenges when it comes to 

trying to rely exclusively on epi data to investigate TB transmission

The fact that transmission is airborne can make it difficult to assess exposure

Assessing exposure in congregate settings can be very complex as well

TB also can have infectious periods that span years and for that reason patient recall may be 

incomplete or unreliable

TB transmission often occurs in impoverished or marginalized communities who are difficult to access

For these reasons, it is helpful to use genotyping which can provide additional, complementary 

information to aid detection and investigation of transmission by identifying cases with genetically 

similar M. tuberculosis isolates that are more likely to be linked by transmission
8

Genotyping examines the DNA of M. tuberculosis 

isolates from TB patients

 The M. tuberculosis bacteria from a TB 

patient is called the patient’s isolate

 Bacteria, including M. tuberculosis, 

have DNA called a genome

 DNA is made up of four different 

nucleotides (abbreviated A, T, C, and G)

 The order of these nucleotides in the 

genome is the DNA sequence

 The genome of M. tuberculosis is over 

4.4 million nucleotides long

TB patient

M. tuberculosis 

isolate from 

patient sample

genome

DNA 

sequence

M. tuberculosis

bacterial cell

...GGATCGGGTGCCTAACTTGG

GTACCGCTTAGGCACCGTCTG

GACGTTGGGCAGGGCCTAG...

DNA

Genotyping examines the DNA of M. tuberculosis isolates from TB patients

The M. tuberculosis bacteria from a TB patient is called the patient’s isolate

Bacteria, including M. tuberculosis, have DNA called a bacterial genome

DNA is made up of four different nucleotides (abbreviated A, T, C and G)

The order of these nucleotides in the genome is the DNA sequence

The genome of M. tuberculosis is over 4.4 million nucleotides long
9

Genotyping can be used to identify TB patients who are more 

likely to be linked by recent transmission

 Changes in the DNA (mutations) occur 

over time, so M. tuberculosis bacteria 

don’t all have the exact same DNA 

sequence

 At the time of transmission, the person 

transmitting the infection and the person 

acquiring the infection will have M. 

tuberculosis with identical DNA sequence

 Genotyping analyzes DNA to identify TB 

patients with similar M. tuberculosis

genomes who are more likely to be linked 

by recent transmission TB patient not linked by 

recent transmission has 

isolate with different 

genotype (green)

TB patients linked by recent transmission have 

isolates with the same genotype (black)

Genotyping can be used to identify TB patients who are more likely to be linked by recent transmission

Changes in the DNA, called mutations, occur over time so M. tuberculosis bacteria don’t all have the 

exact same DNA sequence

At the time of infection, the person transmitting the infection and the person acquiring the infection 

will have M. tuberculosis with identical DNA sequence

Genotyping analyzes DNA to identify TB patients with similar M. tuberculosis genomes who are more 

likely to be linked by recent transmission

In this schematic, transmission is occurring between these two people at the top and they have M. 

tuberculosis isolates with the same genotype (shown in black), but this person at the bottom is not part 

of that transmission chain and has an M. tuberculosis isolate with a different genotype (shown in 

green)
10

Detecting Clusters of Recent Transmission using Genotyping

 2 or more isolates with the same 

genotype are clustered

 Algorithms that consider time and 

space are used to identify clustered 

cases that may be due to recent 

transmission

CDC cluster detection methods

 LLR cluster alerts: Unexpected increase 

in concentration of a genotype in a 

jurisdiction during a 3-year time period

 Large outbreak surveillance: 10 or more 

cases in a 3-year period related by 

recent transmission

M. tuberculosis genotypes in a community

CDC uses M. tuberculosis genotyping data to detect clusters of possible recent transmission

2 or more isolates with the same genotype are considered clustered

This schematic on the right is showing M. tuberculosis genotypes in a community, and we can identify a 

green cluster and a black cluster

Algorithms that consider time and space are then used to identify clustered cases that may be due to 

recent transmission

And CDC has developed cluster detection methods for this purpose

One method is the LLR cluster alert that detects an unexpected increase in concentration of a genotype 

in a jurisdiction during a 3-year time period

Another type of alert is for surveillance of large outbreaks, defined as 10 or more cases in a 3-year 

period related by recent transmission
11

Current M. tuberculosis genotyping is based on only ~1% 

of the genome

Adapted from: Guthrie JL, Gardy JL. Ann N Y Acad Sci. 2016 Dec 23. doi: 10.1111/nyas.13273

M. tuberculosis 

GENType

1 2

1. Spacer Oligonucleotide Typing

2. Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats

Two assays to detect differences in repetitive 

regions of the genome

Current M. tuberculosis genotyping methods cover only about 1% of the genome and are based on 

differences in repetitive regions within the M. tuberculosis genome

The current method combines the results of two assays, spoligotyping and MIRU-VNTR, to give an M. 

tuberculosis GENType

Specifically, spoligotyping is based on the presence or absence of 43 spacer sequences in a direct 

repeat region of the genome

And MIRU-VNTR is based on differences in the number of copies of tandem repeats at 24 regions or loci 

of the genome

Isolates that have the exact same spoligotype and 24 locus MIRU-VNTR pattern are assigned the same 

GENType
12

GENTyping provides low resolution for examining 

genetic relatedness of isolates 

 Examines only a small portion (~1%) of the genome

 Regions examined may not change within a timeframe that is 

useful for understanding recent transmission

 Substantial past transmission of a GENType in a community 

makes it harder to distinguish:

– Cases due to reactivation of infection that was acquired during the past 

transmission versus cases due to recent transmission 

– Separate chains of recent transmission among cases with the same 

GENType

However, GENTyping provides relatively low resolution for examining the genetic relatedness of isolates 

because it only examines a small portion, about 1%, of the genome

The regions of the genome examined by GENTyping may not change within a timeframe that is useful 

for understanding recent transmission

This makes interpretation difficult when there has been substantial past transmission of a GENType in a 

community because it is harder to distinguish cases due to reactivation of infection that was acquired 

during the past transmission versus cases due to recent transmission

And it is harder to distinguish separate chains of recent transmission among cases with the same 

GENType
13

WGS can provide added resolution for 

examining genetic relatedness of isolates 

 Expands coverage of the genome to ~90%

– Captures much more of the genetic changes that occur

Adapted from: Guthrie JL, Gardy JL. Ann N Y Acad

Sci. 2016 Dec 23. doi: 10.1111/nyas.13273

GENTyping

(1% coverage)

WGS

(90% coverage)

Whole-genome sequencing (or WGS) can provide added resolution for examining genetic relatedness 

of isolates by expanding coverage of the genome to about 90%, compared to the 1% that is covered by 

GENTyping

This captures much more of the genetic changes that occur
14

Retrospective WGS for Select GENType clusters

 WGS and phylogenetic analysis of 

>100 clusters nationally to date

– 2012: first WGS of a GENType cluster

– 2014: WGS performed for all GENType

clusters that alerted for large outbreak 

surveillance

– 2016: WGS expanded to include other 

select GENType clusters that could 

inform public health action

CDC has been performing WGS and phylogenetic analysis retrospectively for select GENType clusters 

and has analyzed more than 100 clusters nationally to date

2012 is when we first did WGS of a GENType cluster

In 2014, we started performing WGS for all GENType clusters that alerted for large outbreak 

surveillance

In 2016, we expanded WGS capacity to include other select GENType clusters that could inform public 

health

This map is showing the number of isolates with whole-genome sequencing data for each state or 

territory, with a total of 2,776 isolates having been sequenced as of August 2017, but retrospective 

sequencing is still ongoing
15

Whole-genome single nucleotide polymorphism 

(wgSNP) analysis

 A single nucleotide polymorphism (SNP) is a mutation at a single 

position (A,T,C, or G) in the DNA sequence

 wgSNP analysis uses WGS data to identify SNPs that are useful for 

examining the genetic relationship among isolates 

 SNPs that are identified in the wgSNP analysis are mapped on to a 

phylogenetic tree to diagram the genetic relationship among 

isolates

 The phylogenetic tree can be used to target and inform 

epidemiologic investigation of these cases

The whole-genome sequencing data is used to perform a whole-genome single nucleotide 

polymorphism analysis (or wgSNP analysis)

A single nucleotide polymorphism (or SNP as we call them) is a mutation at a single position (an A, T, C, 

or G) in the DNA sequence

wgSNP analysis uses WGS data to identify SNPs that are useful for examining the genetic relationship 

among isolates

SNPs that are identified in the wgSNP analysis are mapped on to a phylogenetic tree to diagram the 

genetic relationship among isolates

The phylogenetic tree can be used to focus and inform epidemiologic investigation of the cases
16

wgSNP analysis

Reference-based assembly of isolate sequence reads, 

aligning to Mtb reference genome H37Rv

SNPs relative to H37Rv are identified

Uninformative and unreliable SNPs are filtered out to 

produce a list of “high-quality” SNPs

High-quality SNPs are mapped on to a 

phylogenetic tree 

A T G C T G G C A G T C G A C T

A T G C A T C G A C T

T G C A G C A G T C G 

C A G G C A T C G A C T

A G T C G A 

SNPs relative 

to H37Rv Filter 

out

• SNPs in all isolates

• SNPs due to 

assembly errors

• Low confidence 

SNPs

H37Rv

H37Rv

wgSNP analysis is done by first aligning the isolate sequence reads to a reference genome, we use M. 

tuberculosis H37Rv

This is shown on the right here where the sequence reads for the isolate are in black and they are being 

matched up to the sequence of the reference genome shown in grey

Then, SNPs relative to the reference genome H37Rv are identified

This is shown here where the isolate sequence has an A at this position where H37Rv has a T

Then the next step is that uninformative and unreliable SNPs are filtered out to produce a list of highquality SNPs

What we filter out are SNPs that are present in all the isolates in the analysis (because if the SNP is 

present in all the isolates in the cluster being analyzed, the SNP is not informative for understanding the 

genetic relationships among the isolates) 

We also filter out SNPs that are identified because of assembly errors (which means the sequence read 

wasn’t aligned to the correct place of the reference genome)

And low confidence SNPs (for example if there are very few sequence reads that cover the SNP 

position)

Then lastly the high-quality SNPs are mapped on to a phylogenetic tree to diagram the genetic 

relationships between the isolates
17

Guide for interpreting the phylogenetic tree 

 Isolates are shown as circles 

(called nodes)

 Isolates with the same genome 

type are displayed together in one 

node

 Nodes are proportional in length 

to the number of SNPs that differ 

between the isolates

 Lines are labeled with the number 

of SNPs

MRCA

2

2

3

1

1

1

Here is a guide for interpreting the phylogenetic tree

On this tree, the isolates are shown as circles, called nodes (these would usually be labeled with the 

isolate’s accession number)

Isolates that have the same genome type are displayed together in one node

Nodes are connected by lines that are proportional in length to the number of SNPs that differ between 

the isolates 

And the lines are labeled with the number of SNPs

You will see that the tree also has a node labeled MRCA
18

Guide for interpreting the phylogenetic tree 

MRCA = Most Recent Common Ancestor

 Hypothetical genome type (not an 

actual isolate)

 All isolates on the tree are descended 

from this hypothetical genome type

 Serves as a reference point for 

examining the direction of genetic 

change ( )

MRCA

A T G C T G G C A G

A C G C A C G C A G

C T G C T G T C A G

C T G C T G T C A T

C T A A T G T G A G

C T G A T G T G T G

3

2 1

2

1

1

MRCA stands for most recent common ancestor

The MRCA is not an actual isolate but a hypothetical genome type from which all isolates on the tree 

are descended

And so the MRCA serves as a reference point for examining the direction of genetic change which is 

shown with these blue arrows

On this tree, if we start up here at the MRCA and move down this branch, there are three SNPs shown 

in yellow and this isolate has those three SNPs

But if we were to move down this other branch, these two isolates in this node don’t have those three 

yellow SNPs but they have two different SNPs shown in blue

And then from there, we can move up this branch, and this isolate has those same two blue SNPs plus 

one more SNP shown in purple

If we move down this way from the node with the two isolates, then these isolates down here have the 

two blue SNPs plus two red SNPs and then each one has one additional SNP as well – this one has a 

green SNP and this one has a dark blue SNP
19

Guide for interpreting the phylogenetic tree 

Hypothetical Node

 Branching point with no circle

 Represents a hypothetical 

genome type

 No actual isolate with this 

genome type in the analysis

MRCA

C T G C T G T C A G

C T A A T G T G A G

C T G A T G T G T G

3

2

1

2

1

1

C T G A T G T G T G

Hypothetical 

node

And you will see at the bottom here that trees also sometimes have a branching point with no circle

This is called a hypothetical node

The hypothetical node represents a hypothetical genome type but there is no actual isolate with this 

genome type in the analysis

So in this example, if we start here at this node with the two blue SNPs and move down this branch to 

the hypothetical node, you see that the genome type at the hypothetical node has the two blue SNPs 

and the two red SNPs but we don’t actually have an isolate with that genome type in the analysis
20

genetically distant, 

and unlikely to be 

involved in recent 

transmission

=

Guide for interpreting the phylogenetic tree 

6

6

9

10

24

18

MRCA

2

1

1

Closely related isolates, 

which may be involved 

in recent transmission

1

1

We use the tree to examine the number of SNPs that differ between the isolates and identify groups of 

closely related isolates that may be involved in recent transmission

As well as to identify genetically distant isolates that are unlikely to be involved in recent transmission

This helps programs prioritize cases for focus of their epi investigation
21

Guide for interpreting the phylogenetic tree 

 SNP thresholds for categorizing M. tuberculosis isolates as genetically distant or 

closely related have not been formally established for CDC’s wgSNP analysis yet

 Based on CDC’s general experiences using wgSNP analysis for investigating 

recent transmission:

– Isolates with 0 – 5 SNP differences are considered closely related

– Isolates with 6 or more SNP differences are considered genetically distant 

 SNP thresholds will vary depending on the methods used for the wgSNP

analysis, and cannot be compared to thresholds used by other groups with 

different analysis methods

 These recommended SNP thresholds may change as CDC’s wgSNP analysis 

methods are further developed or based on results of a formal validation 

analysis of SNP thresholds

SNP thresholds for categorizing M. tuberculosis isolates as genetically distant or closely related have 

not been formally established for CDC’s wgSNP analysis yet

Based on CDC’s general experiences using wgSNP analysis for investigating recent transmission:

Isolates with 0-5 SNP differences are considered closely related

And isolates with 6 or more SNP differences are considered genetically distant

SNP thresholds will vary depending on the methods used for the wgSNP analysis, and cannot be 

compared to thresholds used by other groups with different analysis methods

These recommended SNP thresholds may change as CDC’s wgSNP analysis methods are further 

developed or based on results of a formal validation analysis of SNP thresholds
22

Phylogenetic tree is not the same as a transmission diagram

Directionality of transmission cannot be inferred from wgSNP analysis alone

MRCA

A, B A

B

B

A

- OR -

An important consideration to keep in mind when looking at these trees is that the phylogenetic trees 

are not the same as transmission diagrams because directionality of transmission cannot be inferred 

from wgSNP analysis alone

If we look at this simple tree on the left, isolates A and B are identical and it’s possible that patient A 

could have transmitted to patient B or patient B could have transmitted to patient A
23

Phylogenetic tree is not the same as a transmission diagram

Consideration #1: Directionality cannot be inferred because cases involved in 

transmission may not be included on tree

MRCA

A, B C

A B

Undetected or 

culture-negative 

case

Directionality of transmission also cannot be inferred because there could be cases involved in 

transmission that are not included in the WGS analysis

For this same tree, it is possible that there is no transmission between patient A and B and transmission 

was through a third case that does not have an isolate on the tree because they are an undetected case 

or were culture-negative
24

Phylogenetic tree is not the same as a transmission diagram

Consideration #2: Directionality cannot be inferred because genetic changes could 

occur between the time of transmission and collection of the patient’s sample

MRCA

A

B A

B

B

A

- OR -

Another consideration is there could be genetic changes that occur between the time of transmission 

and collection of the patient’s sample 

With this tree, it is tempting to think that patient A transmitted to patient B since isolate B is shown to 

have evolved from isolate A

That could be true but it is actually still possible that patient B could have transmitted to patient A
25

Phylogenetic tree is not the same as a transmission diagram

Consideration #2: Directionality cannot be inferred because genetic changes could 

occur between the time of transmission and collection of the patient’s sample

MRCA

A

B

ATACGA

AGACGA

2015 2016 2017

B

A

Transmission ATACGA

Patient B infectious period

(T → G mutation occurs)

Patient A infectious period

(no mutations occur)

X

X

Sample A 

collected

Sample B 

collected

ATACGA

AGACGA

To illustrate this concept with an example, patient B could transmit to patient A in 2015, so both 

patients have this same genome type in 2015

Patient A’s sample is collected about one year later and no mutations have occurred during that time

Patient B’s sample is collected even later and during the time period between transmission and 

collection of the sample, patient B could have a mutation (here a T to G) in his infecting strain, which 

would result in a tree that looks like this

For these reasons, we don’t use the trees to try to infer transmission between patients. We use them 

to identify clusters of cases that may be due to recent transmission
26

Recent transmission is easier to rule out than to 

confirm with WGS

 Even isolates that are closely related or identical by WGS can be due to 

reactivation

– This is because mutations may not occur as frequently during latent 

infection and therefore SNPs may not accumulate

 The phylogenetic tree should be used in conjunction with clinical and 

epidemiologic information to assess recent transmission

It is also important to remember that it is easier to rule out recent transmission than confirm it in using 

WGS

Even isolates that are closely related or identical by WGS can be due to reactivation 

This is because mutations may not occur as frequently during latent infection and therefore SNPs may 

not accumulate

So the phylogenetic tree should be used in conjunction with clinical and epidemiologic information to 

assess recent transmission
27

Summary

 WGS can provide greater resolution than GENType for investigating 

recent TB transmission

 CDC has used WGS retrospectively to examine genetic relatedness of 

isolates clustered by GENType

 wgSNP analysis is performed to produce a phylogenetic tree for 

examining genetic relationships between isolates in a GENType cluster

 The phylogenetic tree should be used in tandem with epidemiologic 

data to identify clusters of closely related isolates that may indicate 

recent transmission, not to draw conclusions about direction of 

transmission among individual patients

In summary, WGS can provide greater resolution than GENType for investigating recent TB transmission

CDC has used WGS retrospectively to examine genetic relatedness of isolates clustered by GENType

wgSNP analysis is performed to produce a phylogenetic tree for examining genetic relationships 

between isolates in a GENType cluster

The phylogenetic tree should be used in tandem with epidemiologic data to identify clusters of closely 

related isolates that may indicate recent transmission, not to draw conclusions about direction of 

transmission among individual patients
28

Part 2

Case Studies: Using WGS to investigate TB cluster 

alerts in California 

Now with that background, I will turn it over to Martin and Tambi who will present the case studies 

using WGS to investigate TB cluster alerts in California
29

Image: http://www.business2community.com/public-relations/make-sure-youre-barking-up-the-right-tree-0320344#!bPmnsw 

Conventional genotyping methods are an important tool to identify and investigate genotyping clusters 

that might be outbreaks or transmission events. 

Sometimes our cluster investigations lead us to previously unknown and important outbreaks where 

intervention is feasible and warranted. We find the red cat, if you will, because conventional 

genotyping leads us to bark up the right tree. 

But, there are limitations of conventional TB genotyping and some of our cluster investigations lead to 

barking up the wrong tree...like the dog in this image...when we investigate genotype clusters of cases 

that might not be linked in a chain of transmission. 

Our talk will describe how WGS can help us make sure we’re barking up the right tree.
30

Tuberculosis in California

* France et al, Am J Epidemiol. 2015

80%

Reactivation 

of remote infection

7%

Importation

Cases diagnosed within 1 

year of arrival in U.S.

13%

Recent

Transmission

CDC plausible source case 

method*

~2,000+ 

TB Cases

in 2016

Some background about TB in CA to provide some context for our talk 

CA reported more than 2,000 cases of TB in 2016, more than any other state in the U.S. 

When we look at why cases occur, there are 3 primary drivers of morbidity. 

• The reactivation of infection acquired a long time ago is responsible for about 80% of 

cases. 

• Importation of TB represents about 7% of the cases.

• Recent TB transmission within CA is estimated to be responsible for about 13% of cases

Our focus today is on recent transmission
31

Impact of WGS on TB Outbreak Classifications, California,

2013 – 2016

164 CDC TB genotype cluster alerts for 

potential outbreaks 

WGS performed for 41 potential outbreaks 

15 (37%) outbreaks 

refuted

26 (63%) outbreaks 

confirmed

Before we dive into our case studies, let’s step back and quickly review how WGS has impacted our TB 

outbreak work in California.

So, in the past 4 years, there have been 164 CDC TB genotype cluster alerts, which represent potential 

outbreaks.

We analyzed data from all of our TB outbreak investigations from 2013 to 2016 and for which we had 

WGS results.

WGS was performed on genotype clusters associated with 41 potential TB outbreaks.

After considering WGS results, along with clinical and epi data, 63% or almost two-thirds of the clusters 

were confirmed as outbreaks. In many of these confirmed outbreaks, WGS helped identify cases that 

could be excluded from further investigation because their TB isolates were genetically distant from the 

outbreak case isolates and unlikely to be linked by recent transmission to the outbreak.

Importantly, more than 1/3 were refuted as outbreaks. All 15 of the refuted outbreaks started as 

suspected outbreaks. These refuted outbreaks are examples where reliance on conventional TB 

genotyping would have led us to bark up the wrong tree—we and local partners would have spent 

precious time and resources looking for epi links between cases that did not exist.
32

Case Study 1

Confirmed outbreak in a high TB 

incidence jurisdiction

Next, I will talk about the first case study which was a confirmed outbreak in a high TB incidence 

jurisdiction.
33

Background

 CDC alert for a TB GENType cluster in County A

– 8 of the 13 cases in California with the GENType lived in County A

 6 cases in County A had known epi links: a confirmed 

outbreak involving a high school and 2 households

 Unknowns

– Are the 2 remaining cases in County A also part of the outbreak?

– Are the 5 California cases outside of County A part of the outbreak?

– Are any of the 7 cases not part of the outbreak linked to each other in 

a separate chain of transmission?

– Where to focus further work to interrupt TB transmission?

 Requested CDC perform WGS

We received a CDC TB genotype cluster alert for a genotype cluster in County A

When we examined this cluster more closely, we found that there were 13 CA cases with the genotype 

and 8 of them lived in County A. The remaining 5 cases were scattered across 4 other counties. 

We notified the County A about the CDC alert. They were already aware that 6 of these cases in their 

County had known epi links. This was actually a confirmed outbreak involving a high school and 2 

households.

But, we and County A were left with some unknowns: 

• Are the 2 other cases in County A part of the outbreak?

• Are the 5 California cases outside of County A part of the outbreak?

• Are any of the 7 cases not part of the outbreak linked to each other in a separate chain 

of transmission?

• Where should we focus further work to interrupt TB transmission for this outbreak?

To help answer these questions we asked CDC to perform WGS on all 13 California cases in the 

genotype cluster.
34

Phylogenetic Tree + Epi Data

1 1

3

7

4

2

3

2

2

MRCA

6 outbreak

TB cases with 

epidemiologic 

links

1

1

Isolate from TB 

case with 

possible epi link 

to County A

5

= County A

Here is the phylogenetic tree for all 13 cases in the genotype cluster. The circles represent isolates from 

the TB cases and lines connect the most-related isolates. The numbers on the lines indicate the 

number of nucleotide differences between isolates. MRCA is the most recent common ancestor which 

is a theoretical isolate from which all of the isolates in the cluster are directly descended. TB cases 

diagnosed in County A are shaded in blue.

The most notable feature of the tree is a group of isolates to the right of the MRCA which is circled by a 

red-dashed line. These are isolates from 6 TB outbreak cases with already-known epidemiologic links. 

The large circle represents 4 isolates with identical sequences (that is, there were no SNPs among the 4 

isolates). Protruding from the large circle are 2 isolates that are closely-related, with only one SNP 

difference.

Also to the right of the MRCA is an isolate from a TB case with possible epi links to County A. The case 

reportedly stayed at a relative’s house in County A for a considerable amount of time. However, the 

isolate is quite genetically distant from the outbreak cases, with 10 or more SNPs from the outbreak 

cases, which suggests that the case is not part of the outbreak.

To the left of the MRCA are 6 isolates, 2 of which are from County A TB cases. You can see from the 

tree that the sequences of the 6 isolates are different from the outbreak cases and are quite different 

from each other.
35

Interpretation

1 1

3

7

4

2

3

2

2

5 MRCA

= County A

= Isolates from TB cases excluded 

from outbreak investigation 

because they are genetically 

distant

1

1

Isolate from TB 

case with 

possible epi link 

to County A

6 outbreak

TB cases with 

epidemiologic 

links

After we add clinical and epi data to the tree, we interpret the results. So, let’s think back to the 

unknowns we listed at the start of this investigation: 

• Are the 2 other cases in County A also part of the outbreak? No, they are genetically 

distant from the nearest outbreak cases by 7 and 10 SNPs, respectively.

• Are the 5 California cases outside of County A part of the outbreak? No, they are 

genetically distant from the nearest outbreak case by 6 to 10 SNPs?

• Are any of the cases that aren’t part of the outbreak linked to each other in a separate 

chain of recent transmission? No, they are 3 to 14 SNPs from each other and there is no 

strong epi or clinical data to suggest they are linked. 
36

New Clustered TB Case

3

MRCA

1

1

1

?

Isolate from a recently 

diagnosed patient in 

County A

= County A

But the story does not end there. Several months after the initial genotype cluster alert, another case 

from County A genotyped into the cluster. 

We notified County A about the new case in the alerted cluster. The county TB program did not know of 

any epi links the new case had to any of the other cases in the genotype cluster. 

The case was a health care worker whose TB skin test recently converted to positive after years of 

negative TB tests. 

WGS was performed on the HCW’s TB isolate and it grouped together with the outbreak cases where 

the red arrow is pointed. 

The county is investigating if/how the case is linked to the outbreak. The concern is that this HCW 

might have acquired TB on the job and that other HCWs and patients might have also been exposed 

and are now at risk for TB.
37

Case Study 1: Public Health Outcomes

 Avoided unnecessary investigation of 7 cases, including 5 

residing in different counties outside of County A

 WGS results enabled continued focus on 6 cases linked by 

recent transmission

 County A intensified work to identify, evaluate, and treat 

contacts to outbreak cases

 County A also investigating the new patient whose TB is 

genetically closely related to the outbreak to determine 

if/how linked to outbreak

The public health outcomes for this investigation were:

• We avoided unnecessary investigation of seven cases, including five residing in different 

counties outside of County A

• WGS results enabled continued focus on six cases linked by recent transmission

• County A intensified work to identify, evaluate, and treat contacts to outbreak cases

• County A also was investigating the new patient whose TB is genetically closely related to the 

outbreak to determine if and how the patient is linked to the outbreak 
38

Case Study 2

Refuted outbreak in a low TB 

incidence jurisdiction

In the second case study, I will present a refuted outbreak in a low TB incidence jurisdiction
39

Background

 In an 8-month period, 4 GENType-matched TB cases were 

initially detected in a rural county

 The county typically has about 10 TB cases per year

 Few GENType clusters in the county

 GENType is relatively uncommon in the state

 Local staff are relatively new to TB control; have 

responsibilities other than TB

We received a CDC TB genotype cluster alert for 4 cases with matching TB genotypes diagnosed in an 8-

month period in a rural county with low TB morbidity—usually this county reports about 10 cases per 

year. GENType clusters are rare in the county. 

In addition, the genotype that alerted in the county is rare in CA and in the US. In the 3 years preceding 

the alert there were only about 17 other cases in the entire US with this genotype. The more 

uncommon a TB genotype is in the US, the more likely it is that clusters of cases with that genotype are

linked by recent transmission. So, the rarity of this TB genotype plus the clustering in time and place in 

a low morbidity TB jurisdiction were concerning for a possible outbreak 

When we looked more closely at the surveillance data for the 4 cases that generated the cluster alert, 

we saw that 2 of the patients were adult men with sputum smear positive TB and 1 had a chest x-ray

showing cavitation—characteristics associated with more infectious forms of TB. Both also were 

reported to be drug users, and we know that cases with that risk factor can present challenges for 

contact investigations.

We also noted that 2 of the culture-confirmed cases were in young, US-born children—one was an 

infant and the other was 5 years old. By definition, TB in young children is a red flag for recent TB 

transmission.

And the county public health staff were all relatively new to TB and were juggling multiple 

responsibilities in addition to TB. 
40

Investigation

? Clinical case

?

?

1

4

6

5

2 3

When we contacted the county TB program, we found their contact investigations of the adult cases 

had identified some epi links. 

County staff knew that Case 1, an adult male, was the likely source case to 2 pediatric cases: one infant 

and a 5 year old child. The dark lines represent definite epi & transmission links. 

They also knew that Case 4, an adult male, was the likely source case for a clinically-diagnosed case in a 

child. We hadn’t been aware of this pediatric case being part of the cluster until we spoke with the 

county. 

Two groups of cases with epi links but no known links between the 2 groups:

1 adult and 2 children ≤ 5 years old (Cases 1, 2, and 3) 

1 adult and a clinically-diagnosed case in a child (Cases 4 & 5) 

So, at that point we had a 5-person suspected TB outbreak. We also learned that the contact 

investigations for the infectious adult cases were challenging and the contact follow-up and 

investigations were incomplete. 

We offered onsite field investigation assistance and the county accepted. We deployed a 2-person team

to the field to assist the county. Despite intensified onsite investigation over a period of about 3 weeks, 

no connections could be found between the 2 groups. 

We asked CDC to perform WGS. A few months later, the genotype for another adult case in the county 

(Case 6) matched to the cluster. No epi links could be found for Case 6. 
41

Phylogenetic Tree

MRCA 16 10

8

8

1

1

1

2

3

4

6

This phylogenetic tree shows the analysis of the WGS results we received back from CDC. 

You can see that isolates from Cases 1, 2, and 3 are genetically closely related and are separated by only 

1-2 SNPs. 

The isolates from Cases 4 and 6 are genetically distant from each other; they are separated by 34 SNPs 

(8+10+16). They are also genetically distant from the isolates of Case 1, 2, and 3.
42

Phylogenetic Tree + Epi Data

MRCA 16 10

8

8

1

1

Clinical case

Genotypically and 

Epidemiologically Linked

Group

Epidemiologically Linked

Group

1

2

3

4 5

6

When we received the phylogenetic tree from CDC, we added the epi link results from the investigation 

we had so far by circling the nodes representing cases with epi links. To help us interpret the results, we 

also added to the tree Case 5 -- the clinically-diagnosed pediatric case that had no TB isolate to 

sequence. 
43

Interpretation

MRCA 16 10

8

8

1

1

Genotypically and 

Epidemiologically Linked

Group

Clinical case

Epidemiologically Linked

Group

Case can be excluded 

from the outbreak 

investigation; isolate is 

genetically distant

No transmission link

1

2

3

4 5

6

After overlaying epi data to the tree, we analyzed and interpreted all available data to understand 

transmission dynamics in this suspected outbreak. 

As you can see in the bottom left portion of the tree, we found that WGS results corroborated the 

already-known transmission links between Adult Case 1 and Pediatric Cases 2 & 3. You can see that the 

TB genome from the two pediatric cases differed by only one SNP from the TB genome of the adult 

source case. This tells us these cases have TB that is genetically closely-related and consistent with 

recent transmission. 

Importantly, the WGS results showed substantial genetic distance of 16 SNPs between these 2 groups 

of cases—the ones at the lower left and upper part of the tree. We had looked hard for an epi link 

between the 2 adult cases 1 and 4 and we were concerned we had missed a connection—potentially a 

drug-related connection--and potentially had failed to identify an important transmission site or other 

contacts who were at risk for TB. 

We and county colleagues were relieved to learn that WGS results corroborated the epi investigation 

finding that there was no link identified between those 2 adult cases; we could stop looking for a recent 

transmission link because one didn’t exist.

We were also happy to learn that Case 6 on the far right side had TB that was genetically distant by 34 

SNPs from the other adult cases. Case 6 is also on the other side of the MRCA meaning that case had 

distinct SNPs that other cases in the genotype cluster did not share, further indicating Case 6 is unlikely 

to be related to the other cases by recent transmission. We were able to exclude Case 6 from the 

investigation. 
44

Case Study 2: Summary

 WGS results showed two separate chains of limited recent 

transmission

 WGS corroborated initial finding that Case 1 was the source 

case to two pediatric cases

 TB program can focus efforts on ensuring that each adult case 

had a complete contact investigation

To summarize, WGS results showed two separate chains of limited recent transmission, WGS 

corroborated initial finding that Case 1 was the source case to two pediatric cases, and the TB program 

can focus efforts on ensuring that each adult case had a complete contact investigation
45

Case Study 2: Public Health Outcomes

 Identified additional high-priority contacts during the 

GENType cluster investigation 

 Intensified follow-up of contacts to ensure evaluation and 

treatment

 Developed local protocol for using new short-course regimen 

for treating TB infection 

 Provided contact investigation training of local health 

department staff

As for public health outcomes for this investigation, we identified additional high-priority contacts 

during the GENType cluster investigation, intensified follow-up of contacts to ensure evaluation and 

treatment, developed local protocol for using new short-course regimen for treating TB infection, and 

provided contact investigation training of local health department staff 
46

WGS Limitations

 No SNP thresholds have been formally validated

 WGS results were generally not available early in 

investigations but turnaround times will improve as lab 

capacity expands

It is important to recognize the limitations of our analysis. These include but aren’t limited to: 

• No SNP threshold has been formally validated as a gold standard for identifying cases likely 

linked by recent transmission. While our experience to date suggests the SNP thresholds we 

used were very concordant with epi links (or lack thereof), a more formal analysis and reporting 

of those concordance data would be another study. 

• WGS results were generally not available early in investigations; most WGS results analyzed 

mid-course in investigations or retrospectively. But, we’re already observing that turn-aroundtimes are improving as lab capacity for TB sequencing and phylogenetic analysis continues to 

expand. Analysis methods are also becoming more automated which will speed turn-aroundtimes. 
47

Conclusions

 Combined analysis of clinical, 

epidemiologic, and phylogenetic data 

can help focus TB investigations

 WGS results can:

– More precisely identify outbreaks and 

outbreak cases

– Avoid unnecessary investigations of 

clusters with cases not linked by recent 

transmission

I hope we have helped describe how the combined analysis of clinical, epi, and phylogenetic data can 

help focus TB investigations.

WGS results can help us make sure we’re barking up the right tree by enabling us to more precisely 

identify outbreaks and outbreak cases. Importantly, these data can also help us avoid unnecessary 

investigations of clusters with cases not linked by recent transmission. 
48

Part 3

Plans for transition to universal prospective WGS

Thanks, Martin and Tambi

Now in part 3, I will briefly describe the plans for transition to universal prospective WGS

A separate presentation covering the details of how universal prospective WGS will be implemented 

will be made available in the future
49

Universal Prospective WGS begins in 2018

 WGS of isolates from all new culture-confirmed cases of TB

 GENType will continue to be analyzed during an initial 3 year 

transition period (2018 – 2020)

– GENType will be reported in TB GIMS

– Cluster alerts will be based on GENType

 In 2021, WGS will become the standard method for genotyping

 WGS data will be used for two separate analyses to examine 

transmission

– wgMLST (whole-genome multi-locus sequence typing)

– wgSNP (whole-genome single nucleotide polymorphism analysis)

Universal prospective WGS begins in 2018 and WGS of isolates from all new culture-confirmed cases of 

TB will be performed

But GENType will continue to be analyzed during an initial 3-year transition period, which will be 2018 

through 2020

During this time, GENType will continue to be reported in TB GIMS and cluster alerts will be based on 

GENType

In 2021, WGS will become the standard method for TB genotyping

WGS data will be used for two separate analyses to examine transmission: wgMLST (which is wholegenome multi-locus sequence typing) and wgSNP (which is whole-genome single nucleotide 

polymorphism analysis)

And I will explain these two analyses in more detail in the following slides
50

Analysis of clustering using WGS data: 

wgMLST vs. wgSNP

wgMLST

(whole-genome multilocus sequence typing)

wgSNP

(whole-genome single 

nucleotide polymorphism)

Level of analysis all isolates isolates in a cluster

Use assigning isolates to a 

wgMLSType that can be 

used for cluster alerting

examining genetic 

relationships among isolates 

Output wgMLSType

(short string of numbers 

similar to a GENType)

phylogenetic tree

Although wgMLST and wgSNP both use WGS data, they differ because wgMLST is a scheme that will be 

applied to all isolates to compare the genomic sequences of the isolates and assign them to a 

wgMLSType that can be used for cluster alerting

The wgMLSType is a standard naming scheme that will be a short string of numbers similar to a 

GENType

On the other hand, we use wgSNP to examine the genetic relationships among isolates in a cluster in 

more detail (and the cluster can be based either on GENType or wgMLSType) and the output is a 

phylogenetic tree that diagrams these genetic relationships
51

Universal prospective WGS begins in 2018

TB Genotyping Methods and Data Flow (2018 – 2020)

Yes No

DNA

WGS data Spoligo MIRU-24

wgMLST wgSNP GENType TB GIMS

Cluster Alert Phylogenetic 

Tree

wgMLSType

(TB GIMS – 2021)

This is what the TB genotyping methods and data flow will look like for the 3-year transition period

During this time, we will be performing whole-genome sequencing as well as conventional genotyping 

with spoligotype and 24 locus MIRU for each isolate

GENType will continue to be analyzed and reported in TB GIMS and GENType will be used for cluster 

alerting

The WGS data will be used for wgMLST and wgSNP analysis

However, wgMLSType won’t be displayed in TB GIMS until after the initial three year overlap period in 

2021

This will allow for the necessary time to adapt algorithms for using wgMLST to define clusters

For clusters that alert during this time based on GENType, wgSNP analysis will be performed to assess 

the potential of recent transmission and this information will be communicated to the state program

For clusters that have had previous retrospective WGS performed, any new isolates in the cluster will 

be added to the analysis and the updated results will be reported to the state program
52

wgMLSType will replace GENType for cluster alerting in 2021

TB Genotyping Methods and Data Flow (2021)

WGS

wgMLST wgSNP

Cluster Alert

Phylogenetic 

Tree

wgMLSType

No Yes

In 2021, WGS will become the standard method performed to identify TB clusters and wgMLSType will 

be used to generate cluster alerts

For clusters that alert, the genetic similarities among the clustered isolates can be examined in more 

detail with wgSNP analysis
53

Acknowledgments

 TB Outbreak Prevention and Control Section, California 

Department of Public Health

 Applied Research Team and Molecular Epidemiology Activity, 

DTBE

 Microbial Diseases Lab, California Department of Public 

Health

 Association of Public Health Laboratories

We would like to thank the TB Outbreak Prevention and Control Section at the California Department of 

Public Health, the Applied Research Team and Molecular Epidemiology Activity at DTBE, the Microbial 

Diseases Lab at the California Department of Public Health, and the Association of Public Health 

Laboratories
54

For more information, contact CDC

1-800-CDC-INFO (232-4636)

TTY: 1-888-232-6348 www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the 

official position of the Centers for Disease Control and Prevention.
55